In recent years, there has been significant effort by the healthcare industry to improve clinical outcomes to patients by providing evidence-based personalized decision support systems and establishing clinical protocols that address the characteristics of both individual patients and patient populations. The healthcare industry has also aimed to provide greater value to patients and their payers (i.e., their insurance carriers and employers) by using evidence-based personalized decision support systems to consider health benefits and cost-effectiveness when recommending treatment regimens.
The above-mentioned efforts by the healthcare industry have been facilitated through the implementation and adoption of health related information technologies (IT), which have led to patient and physician access to electronic medical records and the exchange of electronic data among healthcare providers, laboratories, pharmacies, healthcare managers, and healthcare administrators. Healthcare-related IT platforms have also been instrumental in carrying out biodiscovery, the translation of genetic and molecular data to clinical applications, and the development of prediction models for identifying healthcare-related issues and/or disease outcomes.
In theory, the implementation of healthcare-related prediction models should have a significant impact on healthcare quality and delivery; however, in practice, the types of EMR-generated prediction models and their utility have remained limited to date. Large data sets amassed from multiple data sources, such as healthcare facilities, are often required to build meaningful prediction models with clinical utility. Because the collection, processing, and analysis of such large data sets requires the expenditure of significant human and monetary resources, the benefits of such prediction models remain outside the reach of entities of modest size and/or resources.
In addition to the challenges inherent in physically amassing large quantities of data, the implementation of prediction models also faces challenges related to the protection of personal identifiers that are present in the data sets. Currently, the processing of healthcare information from multiple healthcare facilities to build prediction models requires the transfer of the health data beyond the physical and network boundaries of each healthcare facility. The presence of personal identifiers in the data poses great administrative, logistical, and contractual hurdles to healthcare facilities and any third party entity that is involved in the data processing or analysis. The presence of personal identifiers also raises liability issues and increase the facilities costs to protect against liability. Although personal identifiers may be removed, there are also significant logistical, technical, and financial costs incurred by producing de-identified data sets while preserving the integrity of the data. Because such processes are time-consuming and costly, they inhibit the development of healthcare prediction models.